US5805733A - Method and system for detecting scenes and summarizing video sequences - Google Patents
Method and system for detecting scenes and summarizing video sequences Download PDFInfo
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- US5805733A US5805733A US08/354,367 US35436794A US5805733A US 5805733 A US5805733 A US 5805733A US 35436794 A US35436794 A US 35436794A US 5805733 A US5805733 A US 5805733A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/738—Presentation of query results
- G06F16/739—Presentation of query results in form of a video summary, e.g. the video summary being a video sequence, a composite still image or having synthesized frames
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/74—Browsing; Visualisation therefor
- G06F16/745—Browsing; Visualisation therefor the internal structure of a single video sequence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7847—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using low-level visual features of the video content
- G06F16/785—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using low-level visual features of the video content using colour or luminescence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
Definitions
- This invention relates to the field of video editing and manipulating, and more particularly, to methods and systems for reviewing and analyzing video data.
- a useful framework for analyzing videos is the use of scenes.
- a video is typically made of a number of scenes, each of which contains a number of visually related frames that were recorded from a given vantage point or location. For example, a video of a conversation between several individuals may have various scenes of each person talking, and there may be multiple scenes for a given person speaking throughout the video.
- Scene detection techniques are known, but such techniques can only identify scene transitions, and are unable to consolidate individual scenes that are properly considered to be one scene because of the similarity of the frames in the scenes. Consolidation of scenes is useful to quickly summarize a video.
- Summarizing is distinct from searching, and provides an overview of the contents of a given video by identifying throughout an entire video temporally disjoint scenes that can be considered a single scene.
- summarizing is useful to provide a viewer with the actual flow or change of scenes in a video, since the viewer may be looking for a particular sequence of scenes.
- a human observer would have to review the entire tape, identify and describe each scene separately, noting at what point in the video each scene occurs. This information would then have to be input into a database for later retrieval based on text entry. This is a time consuming process.
- the present invention provides a method for summarizing a video sequence or any other temporally ordered sequence of images into a number of distinct scenes.
- Scene changes are detected using a number of different image analysis techniques in order to identify distinct individual scenes.
- the scenes to be summarized are then compared with selected other scenes. The comparison is performed using a standard error measure on the average color histograms for each scene. The principle employed here is that similar scenes will have substantially similar average color distributions.
- Each group of related scenes is then represented by a frame selected from the set of scenes by displaying the representative frame to the user. Representing each set of related scenes with a representative frame enables the user to view a larger sampling of the video sequence which is otherwise impossible if every scene were displayed to the user.
- related scenes may be consolidated by determining for each scene an average color histogram for all frames in the scene, then comparing the average color histograms of each scene with a fixed number of subsequent scenes, creating in effect, a moving window for comparison.
- Scenes in the window having substantially similar average color histograms form a group of related scenes.
- the moving window is used to limit the number of comparisons at any given time. This results in computational efficiency, by reducing the search space, because all scenes in a group of related scenes need not appear in the window at the same time.
- a representative frame is then taken for each group of related scenes.
- the representative frames can be a frame that is halfway between first and last scenes in the group of redundant scenes.
- the scenes are displayed concurrently, with each frame displayed to the user at the same time in the order in which the scenes appear in the video sequence.
- the scenes are displayed in a three dimensional representation called a "movie bar," which is a rectangular prism comprised of slices of each frame in each scene.
- the scene changes are highlighted to indicate to the user where each scene begins and ends.
- accordion segments may be included in the movie bar to represent a number of related scenes without extending the length of the movie bar beyond the width of the display device being used.
- the user can access scenes represented by the accordion segment by selecting the segment, which then expands to show its scenes.
- FIG. 1 is an illustration of a system of the present invention.
- FIG. 2 is a flowchart of the method of the present invention.
- FIG. 3 is an illustration of the user interface of the present invention.
- FIG. 4 is a flowchart of the process steps for detecting scene changes.
- FIG. 5 is a flowchart of the process steps for consolidating related scenes.
- FIG. 6 is a flowchart for the process steps of displaying the movie bar.
- FIG. 7 is an illustration of one embodiment of a movie bar for indexing scenes in a video sequence.
- the summarizing system 100 includes a processor 109 operatively coupled to a display 103, a pointing device 105, a keyboard 111, a mass storage device 107, and an addressable memory 117.
- the processor 109 is from the 68000 family or PowerPC family produced by Motorola, Inc., and used in the MacintoshTM brand of personal computers manufactured by Apple Computer, Inc.
- the mass storage device 107 is for permanently storing video sequences and the like, including digitized (or digitally produced) video images, or animations.
- the mass storage device 107 is conventional design, with sufficient capacity, preferably in excess of 500 Mb, to store a large number of digitized video sequences.
- the video sequences may be stored in the mass storage device 107 in an image database 113, or other suitable data storage for easy retrieval and indexing.
- Video sequences are input into the image database 113 from the video source 101, which may be a digitizer or conventional graphic design or video production applications.
- the display 103 is also of conventional design and should have sufficient resolution to display at least 640 ⁇ 480 pixels, preferably with at least 16 bit color depth.
- the display 103 is also used to display a user interface to the summarizing system 100, the user interface provided by the user interface controller 125.
- the pointing device 105 may be a mouse, a stylus, a touch-sensitive screen, or a voice activated command processor, or the like device, for providing inputs to the processor 109 via the user interface controller 125, such as for controlling a cursor and other elements provided in the user interface.
- a keyboard 111 is also provided for inputting commands to the summarizing system 100.
- the addressable memory 117 stores a video summarize software application 119 that controls the processor 109 for effecting the methods of the present invention.
- the summarize application 119 includes a scene change detector 121 that detects the scene changes between distinct scenes, having a minimum time duration.
- the related scene detector 123 normalizes the time duration of the scenes to select the most significant scenes for summarizing, and analyzes the selected scenes to determine which scenes are related to each other.
- the user interface controller 125 accepts user inputs for inputting the video sequence to be summarized, displaying the results of the summarization process, specifically the representative frames for each set of related scenes, and the movie bar.
- the movie bar generator 127 creates the movie bar from the scenes. The operation of these various code modules is further described below.
- the user inputs 201 a video sequence into the system 100, or retrieves a video sequence from the mass storage device 107.
- the scene change detector 121 then processes the video sequence to detect 203 individual scenes.
- the related scene detector identifies 205 disparate related scenes, consolidating the video sequence into a smaller number of scenes.
- the related scenes are then time ordered 207, and displayed 209 by the user interface controller 125, showing a representative frame from each set of related scenes.
- An index of the scenes in the video sequence is also displayed 211, here the movie bar is created by the movie bar generator 127.
- the user may then select 213 any number of scenes for viewing in their entirety, the system 100 retrieving 215 each selected scene, and displaying it to the user.
- FIG. 3 shows an illustration of one embodiment of a user interface for displaying summarized scenes, as produced by the user interface controller 125.
- a window 301 includes a collage 303 made up of a representative frame 305 for each set of summarized scenes.
- a representative frame for a set of summarized scenes is taken from the temporally medial scene in the set; alternatively, it can be taken as one of the frames of the longest scene in a set, the longest scene being most indicative of the content of the related scenes.
- the representative frames are ordered according to the temporal position of the scenes they represent in the video sequence.
- An index of the scenes in the video sequence is also displayed, preferably in the form of a movie bar 307.
- the movie bar 307 represents each frame in the entire video sequence as a "slice", the movie bar 307 appearing as a stack of slices.
- a scene 311 is represented in the movie bar 307 as a set of slices, and is visually delineated to indicate the beginning and ending frames of the scenes, for example, with distinct scene breaks 309.
- the user may retrieve a scene or frame by selecting it with the pointing device, either in the movie bar 307 or the collage 303.
- the creation of the movie bar 307 is further described below with respect to FIG. 6.
- FIG. 4 there is shown a flowchart for one method of detecting 203 scene changes with the scene change detector 121.
- the frames of the video sequence are input 401 one by one.
- the scene change detector 121 generates 403 a color histogram for each frame F.
- the scene change detector 121 uses bin segmentation of the HSV color space, with each bin representing 10° of hue and 10% change in saturation, resulting in 360 color bins.
- a Chi-squared value is computed 405 by the scene change detector 121 for each pair of adjacent frames F n , F n+1 to determine if their color histogram distributions are similar.
- the color histograms are significantly dissimilar, and likely to indicate a scene change, since frames in a given scene typically have very similar color histograms.
- frame Fn is marked 409 as the end of one scene M
- frame F n+1 is marked as the beginning of another scene M+1.
- Frame Fn+1 is checked 411 if it is at the end of the video sequence, and if not, the scene change detector increments 413 to the next frame F n+1 .
- the scene change detector 121 has processed all frames of the video sequence for scene changes, the scene change detector 121 returns a set of pointers to the frames marked as scene changes to allow further processing.
- the time duration of each scene M is then determined from the difference in frame numbers between the first and last frames of a scene.
- motion compensated pixel differences can be used by first calculating a set of motion vectors, using conventional techniques, for a given frame F n , predicting a following frame F n+1 using the motion vectors, and then taking a prediction error as the summed squared differences in luminance values over all pixels in the predicted frame F n+1 and the actual frame F n+1 . If the prediction error exceeds a predetermined threshold, then there is a scene change between F n an F n+1 which can be handled as shown above.
- this method can be further enhanced by performing a histogram equalization on the frames of the video sequence.
- motion vectors can be used to generate prediction errors for edge maps between frames.
- the scene change detector 121 may combine all three motion based methods to provide robust detection of scene changes.
- a neural network 122 may be employed within the scene change detector 121.
- the inputs to the neural net 12 include Chi-squared values for pairs of frames, as above, normalized motion compensated pixel differences, and global measurements, such as mean luminance, variance, entropy for each frame, and the like, and differences between these values for pairs of frames.
- the weights for the neural net 122 are determined using a conventional backward propagation procedure, or other suitable learning or update procedure, using a training video sequence having a variety of scenes and scene changes. Backward propagation here essentially performs a "surface fitting" so as to minimize the reference signal, here the training video with "correct” scene changes previously selected, and the output of the neural net 122, for all the training sequences.
- the weights are set at the end of the training, and are used from then on to detect scene changes.
- the neural net 122 is not adapted to new incoming video sequences, due to computational requirements, though in alternative embodiments of the neural net 122 based on higher performance processors, such adaptation may be readily performed.
- the neural network can be periodically re-trained.
- FIG. 5 there is shown a flowchart of a method of consolidating scenes to create a summarized video sequence. Because a lengthy video sequence may have a large number of scenes, it is preferred to summarize a video sequence by selecting those scenes for presentation to a user that probably have the most significant content. One way of estimating which scenes are most significant is by the length of the scene, as longer scenes typically are most important to the overall video sequence. Accordingly, the time duration of all of the detected scenes is normalized 501 in order to determine those scenes which are most significant. In the preferred embodiment normalizing is done by determining the mean and standard deviation for the time duration of all identified scenes. A lower bound for the time duration is set at the greater of the length of one frame or the mean time duration minus three standard deviations.
- An upper bound for time duration is similarly set at the mean time duration plus three standard deviations.
- the time duration of all scenes is then normalized between 1 and 0. Setting the upper and lower bounds for time prior to normalizing ensures that scenes with outlier time durations, either unusually long scenes or unusually short scenes, have a normalized time duration of 1.0 or 0.0, respectively.
- those scenes with a normalized time duration greater than a defined value are selected 503 as the scenes for consolidating. In this manner, m% of the selected scenes can be selected on the basis of the normalized time duration alone, and (100-m)% can be selected as a weighted sum of normalized time duration and scene motion measures.
- the related scene detector 123 determines 505 an average color histogram for each scene by summing the color histograms of each frame in the scene, and then dividing the summed histogram by the number of frames in the scene. This average color histogram of each scene is then analyzed to determine its similarity to nearby scenes in a moving window of scenes, in order to determine if it can be consolidated with related scenes.
- a moving window is used because it is computationally efficient, and reduces need to compare all scenes in the video sequence with each other.
- the window practically reflects the structure of video sequences, since scenes at remote portions of a video sequence are unlikely to be related, and if there is a repetition of a scene, it is generally significant enough to be included in the summary of the video. Accordingly, for each window S of n scenes (n can be predetermined or set by the user), a Chi-square test is performed 509 on each pair of scenes Si and Sj in the window, using the average color histograms for the scenes. The X 2 values for each pair of scenes are sorted 511. Each X 2 value is then compared 513 to a predetermined threshold.
- the scene Si and Sj for the X 2 value have significantly similar average color histograms, and thus are to be part of a single scene.
- the threshold may be set by the user, with lower thresholds resulting only very similar scenes being identified as related, and a higher threshold resulting in a wider variance in similarity. Accordingly, Si, and Sj are marked 515 as related and the bounding frame numbers for each scene are stored, so that any or all of the parts of the scenes can be retrieved.
- the X 2 values for all scenes in the window S are checked, as each scene may be related to multiples scenes in the window. While the Chi-squared test is used in the preferred embodiment, other standard error measures may also be suitably used.
- This process is repeated 507 for each window of n scenes, beginning with the first scene that was selected 503.
- the window is "advanced" by one scene each time, though in alternate embodiments, a larger step may be taken between windows.
- the total scene time is determined 519, and the frame Fmid that is the midpoint of all the scenes is chosen 521 as a representative frame for all the related scenes.
- the middle frame of the longest scene in each set of related scenes can be used as Fmid.
- the related scenes are time ordered 207, and the Fmid frames for each set of scenes are displayed 209 to the user by the user interface controller 125. The user may view a scene by selecting 213 one of the representative frames with the pointing device 105, the system 100 retrieving 215 and displaying the scenes associated with the representative frame.
- FIG. 6 there is shown a flowchart for creating an index of the video sequence for display 211 to the user.
- This flowchart describes the operation of the movie bar generator 127.
- the index is displayed 211 in the form of a three dimensional movie bar 307, as shown in FIG. 3.
- the movie bar 307 is created by taking a vertical and horizontal slice, or set of pixels, from each frame in the video.
- the vertical slice may be the far left hand pixel column of the frame, and the horizontal pixel slice may be the top row of the frame.
- the slices may taken at the horizontal and vertical centers of the frame.
- Each slice is then subsampled 603 by taking every Nth pixel of the slice. This reduces the resolution of the slices by N, in order to accommodate the compacted appearance of the movie bar 307. Low pass filtering may be performed on each slice prior to subsampling.
- the movie bar 307 can display 211 the most popular colors in the frame, for example, with most popular color is shown as the vertical slice of the frame, and the second most popular color is shown as the horizontal slice.
- slicing 601 and subsampling 603 steps are replaced by taking a color histogram of each frame and sorting the histogram by frequency.
- the color histogram may have already been computed for scene change detection, and so may be used here, otherwise it is determined as needed.
- the movie bar 307 is drawn 605 by placing the vertical slices side by side, and the horizontal slices at an appropriate angle to simulate the appearance of a three dimensional rectangular prism, as illustrated in FIG. 3.
- the utility of the movie bar 307 is enhanced by delineating 607 scene boundaries by using brightly colored slices at the location for each scene change.
- a moving arrow or other indicator can be displayed 609 to show the location or position of the current scene and frame that the user is viewing at the time. The user may retrieve for display 617 a scene by selecting 613 one of the slices shown in the movie bar 307.
- the movie bar 307 may be further enhanced for the users convenience when summarizing very long video sequences. In such a video sequence, it is likely that the movie bar 307 would become much longer than the width of the display. In such instances, a conventional technique for showing the movie bar 307 would be to add a horizontal scroll bar to the movie bar 307, for accessing additional scenes. However, this technique would not allow the user to comprehend the entire summarized video sequence at one time, and would impede the user in attempting to access various scenes quickly and efficiently.
- FIG. 7 schematically illustrates one embodiment of a movie bar using accordion segments.
- Each accordion segment 701 represents a set of related scenes 309, such as alternating shots of two persons during a conversation.
- the movie bar 307 expands 615 to show the slices for each frame in the set of scenes 309, as in the regular movie bar 307.
- the user clicks 613 on the expanded scene again it compresses 615 back to the accordion segment 701.
- a fraction e.g. 25/5060
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